Every single day, a massive amount of data is generated by different medical data sources. Processing this wealth of data is indeed a daunting task, and it forces us to adopt smart and scalable computational strategies, including machine intelligence, big data analytics, and data classification. The authors can use the Big Data analysis for effective decision making in healthcare domain using the existing machine learning algorithms with some modification to it. The fundamental purpose of this article is to summarize the role of Big Data analysis in healthcare, and to provide a comprehensive analysis of the various techniques involved in mining big data. This article provides an overview of Big Data, applicability of it in healthcare, some of the work in progress and a future works. Therefore, in this article, the use of machine learning techniques is proposed for real-time diabetic patient data analysis from IoT devices and gateways.
Top1. Introduction
The Internet of Things (IoT) is a computing concept that describes a future where every day physical objects will be connected to the Internet and be able to identify themselves to other devices. This paper presents a review of literature on the subject of the IoT technologies and their applications domains and the futuristic research areas. Several research studies have addressed and developed this topic with detailed studies synthesis about the fields of application of internet of things, and general visions (Gubbi, Buyya, Marusic, & Palaniswami, 2013). Other papers summarize the applications of IoT in the healthcare industry and identify the intelligentization trend and directions of future research in this field (Yin, 2016).
Over the last two decades, we have seen an enormous amount of growth in data. The data has been doubling every two years since 2011. As a result of this technological revolution, big data is becoming an important issue in the sciences, governments, and enterprises increasingly. Big Data is a data set, which is difficult to capture, store, filter, share, analyze and visualize on it with current technologies (Young, Min, Wenixa, & Depeng, 2015).
By understanding, processing and utilizing the knowledge and information hidden in Big Data concerning health issues and disease trends in certain population, we can find solutions, with which, we can live longer and healthier (Lloret, Parra, Taha, & Tomás, 2017, 2017). Big data analytics improve health care insights in many aspects shown in Figure 1.
Benefits to Patients: Big Data in healthcare is being used to predict epidemics, cure disease, improve quality of life and avoid preventable deaths. With the world’s population increasing and everyone living longer, models of treatment delivery are rapidly changing, and data are driving many of the decisions behind those changes. Big data can help patients make the right decision in a timely manner. From patient data, analytics can be applied to identify individuals that need “proactive care” or need a change in their lifestyle to avoid health condition degradation. For example, patients in early stages of some diseases (e.g., heart failure often caused by some risk factors such as hypertension or diabetes) should be able to benefit from preventive care thanks to Big data.
Benefits to Researchers and Developers (R & D): R & D contribute to new algorithms and tools, such as the algorithms by Google, Facebook, and Twitter that define what we find about the health system. Google, for instance, has applied algorithms of data mining and machine learning to detect influenza epidemics through search queries. R & D can also: Enhance predictive models to produce more devices and treatment for the market, and can give statistical tools and algorithms to improve the clinical trial design and patient recruitment to better match treatments to individual patients, thus reducing trial failures and speeding new treatments to market.
Benefits to healthcare Providers: can analyze disease patterns and tracking disease outbreaks and transmission to improve public health surveillance and speed response, Turning large amounts of data into actionable information that can be used to identify needs, provide services, and predict and prevent crises, Capture and analyze in real-time large volumes of fast-moving data from in-hospital and in-home devices, for safety monitoring and adverse event prediction, also providers can Apply advanced analytics to patient profiles to identify individuals who would benefit from proactive care or lifestyle changes, for example, those patients at risk of developing a specific disease (e.g., diabetes) who would benefit from preventive care (Sendra, Parra, Lloret, & Tomás, 2018).